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Intelligent Decision Support Systems: A Summary. H. Munoz-Avila. Specification. 1. Retrieve. 5. Retain. New Slides. Repository of Presentations: 5/9/00: ONR review 8/20/00: EWCBR talk 4/25/01: DARPA review. Slides of Talks w/ Similar Content. 4. Review. New Case. Talk@ CSE335/435.
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Intelligent Decision Support Systems: A Summary H. Munoz-Avila
Specification 1. Retrieve 5. Retain New Slides • Repository of Presentations: • 5/9/00: ONR review • 8/20/00: EWCBR talk • 4/25/01: DARPA review Slides of Talks w/ Similar Content 4. Review New Case Talk@ CSE335/435 Revised talk 3. Revise First draft 2. Reuse • Retrieval • case similarity • case retrieval • programming project • Customer Support (SicongKuang) • Recommender Systems (Eric Nalisnick) Case-Based Reasoning • Retain • Indexing • K-D trees • Induction • Maintenance of CBR systems (Aziz Doumith) Example: Slide Creation - - 12/7/12: talk@ CSE 335 • Reuse • Adaptation • Rule-based systems • Plan adaptation Knowledge Containers (GiulioFinestrali)
User-System Interactions in Case Base Reasoning (Sicong Kuang) Knowledge Representation (Prof. Jeff Heflin) Ontology DL Reasoner • Two tasks: • Problem Acquisition Task • Experience Presentation Task • Adaptable dialog strategy • Illustrate two applications • Web-based CBR system • GE call center Inferred Hierarchy table & view creation Database operation
Decision making and finance (KonstantinosHatalis) An application of CBR to oil drilling (Dustin Dannenhauer) • CBR for market surveillance • Input: Transaction info… • Output: unusual trend • Residential profit valuation • Ideal CBR application (i.e., compare similar houses) • Use fuzzy logic in similarity computations • Bank lending decision • Economic sentiment: optimistic, neutral, … • Complexities of oil drilling • Data mining couldn’t be made to work here • Model-based solution didn’t worked well either • CBR solution worked well • Cases describe specific situations • Radar interface when potential anomalies occur
Intelligent Tutoring Systems (TashwinKhurana) Maintenance of CBR systems (Aziz Doumith) • Conventional model doesn’t work (“one model fits all”) • Solution: use cases for the student and domain models • Introduce personalization • Cases can contain • Complete solutions, or • Snippets of solutions • Provides ability for novel combinations • 3-level experience base: from specific to generic knowledge • Categories of revision: corrective & adaptive • Provenance: • history of cases • Where does cases came from? • Event-condition-action
Bio-control: pest control, fish farms, and others (ChoatInthawongse) Knowledge Containers (GiulioFinestrali) • Explains (in part) success in fielded CBR applications • Vocabulary • Similarity Measure • Case Base • Solution Transformation • Learning of these containers • PAC learning • Grasshoppers control • Balance: cost vs reward • Cases include features such as grasshopper density and temperature • Temporal projection
Help-desk systems (SiddarthYagnam) Recommender Systems (Eric Nalisnick) • Collaborative filtering • Issues: scalability, extremely popular/unpopular items,… • Knowledge-based collaborative filtering • Use similarity to address some of these issues • Hybrid Item-to-Item Collaborative Filtering • Text-based help desk systems • Mixed results: reduced to keywords search • Rule-based help desk systems • Ask relevant questions but are difficult to create • CBR-based help desk systems • Alleviate the knowledge engineering effort • Go beyond keyword search • Result in significant reductions of call-in
Music composition (Hana Harrison) Decision Support Systems in Medicine (Jennifer Bayzick) • Domain complexities: lots of data, individuals vs. environment • Potential applications: diagnosis prognosis • Challenge: acceptability • iNN(k): variant of kNN that requires fewer features • An ITS for medicine • History of efforts in the field • Performance Systems: expressiveness of music • ExpressTempo: make tempo transformations sound natural • Similarity captures perceived similarity between performances • SaxEx: Generates expressive performances of melodies • Uses deep background musical knowledge
Design Project • Hana. Restaurant recommender. • Zach. Web search using link (context) • Qin: Amazon recommender. • Giulio. Learning explanations in interactive system. • Jen. Intelligent music player. • Nick. State park recommender. • Sean. Music completion. • Drew. Texas Hold’em. • Tashwin– Intelligent Tutoring Systems. • Yu Yu. Search engine for university events. • Marek. Windows Vista assistant. • Sicong. Case-based support for business. • Choat. Using CBR to alleviate business processes • Siddarth. Augmenting Lehigh LTS system • Aziz. Vacation recommender. • Kostas. Portfolio managing system. • Dustin. College admissions.
The Summary Programming project • AI • Introduction • Overview • IDT • Attribute-Value Rep. • Decision Trees • Induction • CBR • Introduction • Representation • Similarity • Retrieval • Adaptation • Rule-based Inference • Rule-based Systems • Expert Systems • Synthesis Tasks • Planning • Rule inference • Uncertainty (MDPs, • Fuzzy logic) (the end) • Applications to IDSS: • Analysis Tasks • Help-desk systems • Classification • Diagnosis • Recommender systems • Synthesis Tasks • Military planning • Oil drilling, finance, music,.. • Knowledge management
